Ideas2IT Blog

Modern software engineering doesn’t wait until the end to test. Agile Testing threads quality through every phase — aligning people, process, and tooling for a seamless path from backlog to deployment.

AI transformation has officially left the innovation lab and entered the boardroom as a hard executive mandate. With cost pressures mounting, competitive disruption accelerating, and investor expectations demanding measurable outcomes, artificial intelligence is an execution imperative that will define which companies thrive in the next decade.

AI developer tools are everywhere. Your IDE likely has one embedded. Your team has experimented with Copilot, ChatGPT, or both. The promise? 10x productivity. The reality? A few autocomplete wins, followed by inconsistent outcomes, bloated scaffolds, and confusion about when to trust the AI.

This blog explores how Agentic AI systems reshape Agile by embedding intelligence into every phase of the SDLC.

Launching a successful product doesn’t require a fully-featured build from the start. Companies like Instagram, Dropbox, and Airbnb all began their journey with a simple Minimum Viable Product (MVP), using it as a testing ground to validate ideas before scaling.

Steve Jobs once said, "People don't know what they want until you show it to them." This highlights a significant challenge in product development: Customers often can't articulate their needs until they interact with a real product. Prototypes alone don’t offer enough functionality, potentially leading to misunderstandings about a product's actual viability. This is where the concept of a Minimum Viable Product (MVP) comes into play, bridging the gap between concept and market fit.